class_docstr: Meter to calculate top-k recall for single-label or multi-label
  image classification task.
class_docstr_tok: ["Meter","to","calculate","top-k","recall","for","single-label","or","multi-label","image","classification","task","."]
code: def validate(self, model_output_shape, target_shape):
      assert (
          len(model_output_shape) == 2
      ), "model_output_shape must be (B, C) \
              Found shape {}".format(
          model_output_shape
      )
      assert (
          len(target_shape) > 0 and len(target_shape) < 3
      ), "target_shape must be (B) or (B, C) \
              Found shape {}".format(
          target_shape
      )
      assert (
          max(self._topk) < model_output_shape[1]
      ), "k in top_k, for \
              recall_meter cannot be larger than num_classes: \
              {}".format(
          model_output_shape[1]
      )
  
file_path: ClassyVision/classy_vision/meters/recall_meter.py
imports: from typing import Any, Dict, Sequence
  import torch
  from classy_vision.generic.distributed_util import all_reduce_sum
  from classy_vision.generic.util import is_pos_int, maybe_convert_to_one_hot
  from classy_vision.meters import ClassyMeter
  from .None import register_meter
label_desc: Use image node_id 10 for calling the RecallAtKMeter obj's underlying member method code with example usage: obj.validate(model_output_shape, target_shape) without return types